https://github.com/anshuman-02/ai_search_techniques_python
Python implementation of Generate-and-Test and Hill Climbing algorithms, demonstrating core AI search techniques.
https://github.com/anshuman-02/ai_search_techniques_python
ai artificialintelligence generateandtest hillclimbing optimization python searchalgorithms
Last synced: 9 months ago
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Python implementation of Generate-and-Test and Hill Climbing algorithms, demonstrating core AI search techniques.
- Host: GitHub
- URL: https://github.com/anshuman-02/ai_search_techniques_python
- Owner: Anshuman-02
- Created: 2025-01-22T19:39:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-22T19:44:43.000Z (over 1 year ago)
- Last Synced: 2025-07-05T00:06:16.843Z (10 months ago)
- Topics: ai, artificialintelligence, generateandtest, hillclimbing, optimization, python, searchalgorithms
- Language: Python
- Homepage:
- Size: 460 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Implementations of AI Search Techniques: Generate-And-Test and Hill Climbing
## Overview
This repository contains Python implementations of two foundational search techniques in Artificial Intelligence:
1. **Generate-And-Test Algorithm**
2. **Hill Climbing Algorithm**
These algorithms are used to explore and solve optimization problems effectively by navigating a search space.
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## Features
- **Generate-And-Test**:
- Explores all possible solutions and tests them to find the correct or optimal one.
- **Hill Climbing**:
- Iteratively improves the solution by moving to a better neighboring state until a local optimum is reached.
---
## Example Scenarios
- **Generate-And-Test**:
- Finds the correct solution from a set of possibilities, such as solving a puzzle or identifying a valid combination.
- **Hill Climbing**:
- Solves optimization problems like finding the shortest path, maximizing utility, or other similar scenarios.
## Requirements
- Python 3.7 or later
- No additional libraries are required for basic functionality.
## Notes
- The algorithms are implemented in Python for educational purposes.
- Hill Climbing may terminate at a local optimum depending on the problem space.